Chromosomal abnormalities of mesenchymal stromal cells in hematological malignancies
Bibliographic record
Abstract
Historically, hematological malignancies (HMs) and solid cancers were primarily attributed to cell-intrinsic mechanisms. However, overwhelming evidence highlights the crucial role of the tumor microenvironment in this process. Abnormalities in the bone marrow microenvironment (BMM) contribute to the development of HMs and affect patient outcomes. Bone Marrow Mesenchymal Stromal Cells (BM-MSCs) represent one of the key cell types within the BMM. Interestingly, a single specific gene mutation in BM-MSCs is sufficient to disrupt normal hematopoiesis and promote clonal malignant hematopoiesis in mice. Since a particular mutation may be sufficient, attention must also be given to chromosomal abnormalities (CAs), potentially affecting hundreds of genes. Notably, CAs have been identified in the majority of HMs BM-MSCs. CAs have been detected more frequently in BM-MSCs of HMs patients than in healthy donors. The primary explanation for CAs is chromosomal instability (CIN), a phenomenon characterized by increased rates of CAs. CIN can lead to abnormal gene expression, cellular senescence, and inflammation, altering MSCs. It may compromise their anti-tumorigenic functions and shift the BMM towards a supportive or protective state. Despite the importance of CAs and CIN, cytogenetic results in HM-MSCs appear controversial. This review discusses current studies, suggesting that some of the controversies may result from technical limitations. Furthermore, based on the high incidence of CAs and the lack of patterns (randomness), we suggest this is a case of CIN. Therefore, instead of looking for CAs patterns, we must focus on understanding the phenomenon of CIN in these cells. This includes verifying the frequencies of non-clonal CAs, looking for specific CIN mechanisms and distinguishing whether CIN is a driver or a consequence of HMs. To guide future research and address the existing knowledge gaps, we discuss potential approaches to the challenges in studying CAs in HM-MSCs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".